Study On Critical Techniques Of The Brain-Computer Interface Based On Multi-modal EEG

The Brain Computer Interface(BCI) system aims at establishing the correlation between the intention of brain thinking and the behavior, and finally achieving the direct human-computer interaction and control. BCI has a broad development and application prospect in brain cognition and biofeedback training, etc.. Electroencephalogram(EEG) signal has the characteristics of nonlinearity, nonstationarity and susceptivity to external interference. Having high time resolution and real-time response, EEG is the carrier that can comprehensively reflect the electric activity of brain tissue and the functional state of brain.Brain thinking is highly complex. At present, the typical single-mode BCI based on single category of EEG signals can only identify tasks on the level of simple thinking activity with low recognition rate and poor versatility. Aiming at analyzing the thinking tasks from multiple channels, the multi-mode BCI system based on complementarity of the various EEG signals can provide multi-type and multi-task of conscious recognitions and improve the recognition accuracy and versatility of system through appropriate fusion and comprehensive utilization of multi-modal EEG signals. The multi-modal BCI system has become the trend of BCI development. How to rapidly and effectively select the characteristic parameters that can symbolize the conscious task from the complex EEG signals, distinguish different conscious tasks and produce the corresponding control commands is the core for the multi-modal BCI system based on multi-mode EEG signals, and the algorithms for pretreatment, feature extraction and classification are its key technologies.In order to realize the identification of four kinds of conscious commands(left and right hand, left and right foot) in multi-modal BCI system, taking the key algorithms for processing the multi-modal EEG signals based on Steady State Visual Evoked Potential(SSVEP) and Motor Imagery(MI) as the core, improving the recognition accuracy and speed of multi-modal BCI system as the breakthrough point, this thesis constructed a BCI system framework based on multi-modal EEG signals, designed an experimental paradigm for multi-modal BCI system, collected multi-modal EEG signals, designed the key algorithms for pretreatment, feature extraction and classification&recognition and achieved the BCI control of four kinds of conscious commands. The experimental results demonstrated that this system has high recognition accuracy and speed. The main findings include:1) The typical BCI systems based on EEG signal were comparatively studied. The single-mode EEG signal is easily influenced by the subjective factors and has other problems like singleness of feature information and low speed of transmission. In accordance with these problems, the multi-modal EEG signal based on MI and SSVEP was put forward, providing basis for realizing the best classification control of left and right hand, left and right foot in BCI system;2) With identification of four kinds of conscious commands (left and right hand, left and right foot) as the objective, I designed a novel experimental paradigm using the core algorithms for the BCI system based on multi-modal EEG signals. The multi-modal EEG signals of large samples were collected. Twelve acquisition channels including C3, C4, FC3, FC4and Cz were determined. The coherent averaging preprocessing algorithm was selected. After comparison of the feature extraction algorithms of AR model, WT, WPT and HHT, classification identification algorithms of Fisher and SVM, a combined algorithm of AR+HHT feature extraction and SVM classification&identification was designed;3) Targeting at the problems in realizing the multi-modal BCI system, this thesis constructed a BCI system framework that can identify the MI of left and right hand, left and right foot based on the above algorithms, and this framework was realized by programming. A large sample contrast test was designed to verify the validity of the algorithms and system framework. The experimental results demonstrated that the BCI system based on multi-modal EEG signal has high recognition accuracy and speed.